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Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition

机译：通过主成分分析对EEG数据进行降维处理会降低其后续独立成分分解的质量

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Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non-brain source processes and thereby increasing their definition. Dimension reduction by Principal Component Analysis (PCA) has often been recommended before ICA decomposition of EEG data, both to minimize the amount of required data and computation time. Here we compared ICA decompositions of fourteen 72-channel single subject EEG data sets obtained (i) after applying preliminary dimension reduction by PCA, (ii) after applying no such dimension reduction, or else (iii) applying PCA only. Reducing the data rank by PCA (even to remove only 1% of data variance) adversely affected both the numbers of dipolar independent components (ICs) and their stability under repeated decomposition. For example, decomposing a principal subspace retaining 95% of original data variance reduced the mean number of recovered ‘dipolar’ ICs from 30 to 10 per data set and reduced median IC stability from 90% to 76%. PCA rank reduction also decreased the numbers of near-equivalent ICs across subjects. For instance, decomposing a principal subspace retaining 95% of data variance reduced the number of subjects represented in an IC cluster accounting for frontal midline theta activity from 11 to 5. PCA rank reduction also increased uncertainty in the equivalent dipole positions and spectra of the IC brain effective sources. These results suggest that when applying ICA decomposition to EEG data, PCA rank reduction should best be avoided.
机译：独立成分分析（ICA）已被证明是一种有效的数据驱动方法，用于分析EEG数据，从时间和功能上独立的大脑和非大脑源过程中分离信号，从而提高其清晰度。在ICA分解EEG数据之前，通常建议通过主成分分析（PCA）进行降维，以最大程度地减少所需数据量和计算时间。在这里，我们比较了14个72通道单主题EEG数据集的ICA分解，这些数据集是：（i）通过PCA进行初步的尺寸缩减后；（ii）没有应用此类尺寸的缩减后；否则（iii）仅应用PCA。通过PCA降低数据等级（甚至只删除1％的数据差异）会对偶极独立分量（IC）的数量及其在反复分解下的稳定性产生不利影响。例如，分解保留95％原始数据方差的主子空间可使每个数据集恢复的“偶极” IC的平均数量从30个减少到10个，并将中位IC稳定性从90％减少到76％。 PCA排名降低还减少了受试者之间几乎等效的IC数量。例如，分解保留95％的数据方差的主子空间可将占正面中线theta活动的IC簇中代表的对象数量从11减少到5。PCA等级降低也增加了IC等效偶极子位置和光谱的不确定性脑部有效来源。这些结果表明，将ICA分解应用于EEG数据时，最好避免PCA等级降低。

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